人工智能提高了与临床专家相比,在希尔施普朗格病中对比液囊的诊断准确度
Paulina Vargova1, Matej Varga2, Beatriz Izquierdo-Hernández3
1Department of Pediatric Surgery, Miguel Servet University Hospital, Zaragoza, Spain.
一个深度神经网络 (DNN) 在解释希尔施普朗格病 (HD) 的对比阴囊 (CE) 图像方面表现出高准确度. 人工智能辅助解读为临床医生提供了更好的特异性和可比的敏感性,支持其在疾病诊断中的作用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科手术 儿科手术
背景情况:
- 对比清洗 (CE) 是怀疑赫施普朗格病 (HD) 的标准诊断工具.
- 深度学习 (DL) 提供了标准化图像评估和帮助儿科放射学临床决策的潜力.
- 对DL模型与专家临床医生的诊断性能进行评估对于临床采用至关重要.
研究的目的:
- 评估深度神经网络 (DNN) 的诊断性能,以解释疑似HD的CE图像.
- 为了比较DNN的解释,有和没有临床数据,与儿科外科医生和放射科医生.
- 评估DNN,临床医生和专家之间的观察者间协议共识.
主要方法:
- 追溯分析了15岁以下患者的1471张CE图像.
- 由DNN,儿科放射科医生和外科医生对218张测试图像进行独立审查.
- 使用ROC曲线,PR曲线和Fleiss' kappa进行绩效评估,以获得观察者之间的一致性,并以直肠活检作为参考标准.
主要成果:
- DNN实现了高的诊断准确性 (AUC-ROC = 0.87),使用结合的前后和侧图像 (AUC-ROC = 0.92) 提高了性能.
- 整合临床数据提高了DNN的敏感性和负预测值.
- 人工智能辅助解读显示出更高的特异性和对专家临床医生的可比敏感性,与外科医生有很强的一致性 (科恩的卡帕=0.73).
结论:
- 人工智能辅助的CE解释显示,有可能提高希尔施普朗格病评估中的诊断准确性.
- DNN的一致表现和与专家临床医生的协议支持其在改善CE评估方面的实用性.
- 深度学习模型可以作为有价值的工具,以支持HD诊断的儿科放射学中的临床决策.
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